The process of trading financial instruments may be viewed broadly as proceeding through a cycle as shown in FIG. 1. At the top of the cycle is the exchange which is responsible for matching up offers to buy and sell financial instruments. Exchanges disseminate market information, such as the appearance of new buy/sell offers and trade transactions, as streams of events known as market data feeds. Trading firms receive market data from the various exchanges upon which they trade. Note that many traders manage diverse portfolios of instruments requiring them to monitor the state of multiple exchanges. Utilizing the data received from the exchange feeds, trading systems make trading decisions and issue buy/sell orders to the financial exchanges. Orders flow into the exchange where they are inserted into a sorted “book” of orders, triggering the publication of one or more events on the market data feeds.
Exchanges keep a sorted listing of limit orders for each financial instrument, known as an order book. As used herein, a “limit order” refers to an offer to buy or sell a specified number of shares of a given financial instrument at a specified price. Limit orders can be sorted based on price, size, and time according to exchange-specific rules. Many exchanges publish market data feeds that disseminate order book updates as order add, modify, and delete events. These feeds belong to a class of feeds known as level 2 data feeds. It should be understood that each exchange may be a little different as to when data is published on the feed and how much normalization the exchange performs when publishing events on the feed, although it is fair to expect that the amount of normalization in the level 2 feed is minimal relative to a level 1 feed. These feeds typically utilize one of two standard data models: full order depth or price aggregated depth. As shown in FIG. 2(a), full order depth feeds contain events that allow recipients to construct order books that mirror the order books used by the exchange matching engines. This is useful for trading strategies that require knowledge of the state of specific orders in the market.
As shown in FIG. 2(b), price aggregated depth feeds contain events that allow recipients to construct order books that report the distribution of liquidity (available shares) over prices for a given financial instrument.
Order book feeds are valuable to electronic trading as they provide what is generally considered the fastest and deepest insight into market dynamics. The current set of order book feeds includes feeds for order books of equities, equity options, and commodities. Several exchanges have announced plans to provide new order book feeds for derivative instruments such as equity options. Given its explosive growth over the past several years, derivative instrument trading is responsible for the lion's share of current market data traffic. The Options Price Reporting Authority (OPRA) feed is the most significant source of derivatives market data, and it belongs to the class of feeds known as “level 1” feeds. Level 1 feeds report quotes, trades, trade cancels and corrections, and a variety of summary events. For a given financial instrument, the highest buy price and lowest sell price comprise the “best bid and offer” (BBO) that are advertised as the quote. As an exchange's sorted order book listing changes due to order executions, modifications, or cancellations, the exchange publishes new quotes. When the best bid and offer prices match in the exchange's order book, the exchange executes a trade and advertises the trade transaction on its level 1 market data feed. Note that some amount of processing is required prior to publishing a quote or trade event because of the latency incurred by the publisher's computer system when processing limit orders to build order books and identify whether trades or quotes should be generated. Thus, level 1 data feeds from exchanges or other providers possess inherent latency relative to viewing “raw” order events on order book feeds. A feed of raw limit order data belongs to a class of feeds known as “level 2” feeds.
In order to minimize total system latency, many electronic trading firms ingest market data feeds, including market data feeds of limit orders, directly into their own computer systems from the financial exchanges. While some loose standards are in place, most exchanges define unique protocols for disseminating their market data. This allows the exchanges to modify the protocols as needed to adjust to changes in market dynamics, regulatory controls, and the introduction of new asset classes. The ticker plant resides at the head of the platform and is responsible for the normalization, caching, filtering, and publishing of market data messages. A ticker plant typically provides a subscribe interface to a set of downstream trading applications. By normalizing data from disparate exchanges and asset classes, the ticker plant provides a consistent data model for trading applications. The subscribe interface allows each trading application to construct a custom normalized data feed containing only the information it requires. This is accomplished by performing subscription-based filtering at the ticker plant.
In traditional market data platforms known to the inventors, the ticker plant may perform some normalization tasks on order book feeds, but the task of constructing sorted and/or price-aggregated views of order books is typically pushed to downstream components in the market data platform. The inventors believe that such a trading platform architecture increases processing latency and the number of discrete systems required to process order book feeds. As an improvement over such an arrangement, an embodiment of the invention disclosed herein enables a ticker plant to perform order feed processing (e.g., normalization, price-aggregation, sorting) in an accelerated and integrated fashion, thereby increasing system throughput and decreasing processing latency. In an exemplary embodiment, the ticker plant employs a coprocessor that serves as an offload engine to accelerate the building of order books. Financial market data received on a feed into the ticket plant can be transferred on a streaming basis to the coprocessor for high speed processing.
Thus, in accordance with an exemplary embodiment of the invention, the inventors disclose a method for generating an order book view from financial market depth data, the method comprising: (1) maintaining a data structure representative of a plurality of order books for a plurality of financial instruments, and (2) hardware-accelerating a processing of a plurality of financial market depth data messages to update the order books within the data structure. Preferably the hardware-accelerating step is performed by a coprocessor within a ticker plant. The inventors also disclose a system for generating an order book view from financial market depth data, the system comprising: (1) a memory for storing a data structure representative of a plurality of order books for a plurality of financial instruments, and (2) a coprocessor configured to process of a plurality of financial market depth data messages to update the order books within the data structure.
Using these order books, the method and system can also produce views of those order books for ultimate delivery to interested subscribers. The inventors define two general classes of book views that can be produced in accordance with various exemplary embodiments: stream views (unsorted, non-cached) and summary views (sorted, cached). Stream views provide client applications with a normalized stream of updates for limit orders or aggregated price-points for the specified regional symbol, composite symbol, or feed source (exchange). Summary views provide client applications with multiple sorted views of the book, including composite views (a.k.a. “virtual order books”) that span multiple markets.
In an exemplary embodiment, stream views comprise a normalized stream of updates for limit orders or aggregated price-points for the specified regional symbol, composite symbol, or feed source (exchange). Following the creation of a stream subscription, a ticker plant can be configured to provide a client application with a stream of normalized events containing limit order or price point updates. As stream subscriptions do not provide sorting, it is expected that stream view data would be employed by client applications that construct their own book views or journals from the normalized event stream from one or more specified exchanges.
An example of a stream view that can be generated by various embodiments is an order stream view. An order stream view comprises a stream of normalized limit order update events for one or more specified regional symbols. The normalized events comprise fields such as the type of update (add, modify, delete), the order price, order size, exchange timestamp, and order identifier (if provided by the exchange). Another example of an order stream view is an order exchange stream view that comprises a stream of normalized limit order update events for one or more specified exchanges or clusters of instruments within an exchange. The normalized events comprise fields such as the type of update (add, modify, delete), the order price, order size, exchange timestamp, and order identifier (if provided by the exchange).
Another example of a stream view that can be generated by various embodiments is a price stream view. A price stream view comprises a stream of normalized price level update events for one or more specified regional symbols. The normalized events comprise fields such as the type of update (add, modify, delete), the aggregated price, order volume at the aggregated price, and the order count at the aggregated price. Another example of a price stream view is a price exchange stream view. A price exchange stream view comprises a stream of normalized price level update events for one or more specified exchanges or clusters of instruments within an exchange. The normalized events comprise fields such as the type of update (add, modify, delete), the aggregated price, order volume at the aggregated price, and order count at the aggregated price.
Another example of a stream view that can be generated by various embodiments is an aggregate stream view. An aggregate stream view comprises a stream of normalized price level update events for one or more specified composite symbols. The normalized events comprise fields such as the type of update (add, modify, delete), the (virtual) aggregated price, (virtual) order volume at the aggregated price, and (virtual) order count at the aggregated price.
As explained in the above-referenced and incorporated U.S. Patent Application Publication 2008/0243675, a regional symbol serves to identify a financial instrument traded on a particular exchange while a composite symbol serves to identify a financial instrument in the aggregate on all of the exchanges upon which it trades. It should be understood that embodiments of the invention disclosed herein may be configured to store both regional and composite records for the same financial instrument in situations where the financial instrument is traded on multiple exchanges.
Summary views provide liquidity insight, and the inventors believe it is highly desirable to obtain such liquidity insight with ultra low latency. In accordance with an embodiment disclosed herein, by offloading a significant amount of data processing from client applications to a ticker plant, the ticker plant frees up client processing resources, thereby enabling those client resources to implement more sophisticated trading applications that retain first mover advantage.
An example of a summary view that can be generated by various embodiments is an order summary view. An order summary view represents a first-order liquidity view of the raw limit order data disseminated by a single feed source. The inventors define an order summary view to be a sorted listing comprising a plurality of individual limit orders for a given financial instrument on a given exchange. The sort order is preferably by price and then by time (or then by size for some exchanges). An example of an order summary view is shown in FIG. 3.
Another example of a summary view that can be generated by various embodiments is a price summary view. A price summary view represents a second-order liquidity view of the raw limit order data disseminated by a single feed source. The inventors define a price summary view to be a sorted listing comprising a plurality of price levels for a given financial instrument on a given exchange, wherein each price level represents an aggregation of same-priced orders from that exchange. The price level timestamp in the summary view preferably reports the timestamp of the most recent event at that price level from that exchange. An example of a price summary view is shown in FIG. 4(a). Note that a price summary view produced by an embodiment disclosed herein may be limited to a user-specified number of price points starting from the top of the book.
Another example of a summary view that can be generated by various embodiments is a spliced price summary view. A spliced price summary view represents a second-order, pan-market liquidity view of the raw limit order data disseminated by multiple feed sources. The inventors define a spliced price summary view to be a sorted listing comprising a plurality of price levels for a given financial instrument across all contributing exchanges where each price level represents an aggregation of same-priced orders from a unique contributing exchange. The price level timestamp in the spliced price summary view preferably reports the timestamp of the most recent event at that price level for the specified exchange. An example of a spliced price summary view is shown in FIG. 4(b). Note that a spliced price summary view produced by an embodiment disclosed herein may be limited to a user-specified number of price points starting from the top of the book.
Another example of a summary view that can be generated by various embodiments is an aggregate price summary view. An aggregate price summary view represents a third-order, pan-market liquidity view of the raw limit order data disseminated by multiple feed sources. The inventors define an aggregate price summary view to be a sorted listing comprising a plurality of price levels for a given financial instrument where each price level represents an aggregation of same-priced orders from all contributing exchanges. The price level timestamp in the aggregate price summary view preferably reports the timestamp of the most recent event at that price level from any contributing exchange. An example of an aggregate price summary view is shown in FIG. 4(c). Note that an aggregate price summary view produced by an embodiment disclosed herein may be limited to a user-specified number of price points starting from the top of the book.
The inventors further note that financial exchanges have continued to innovate in order to compete and to provide more efficient markets. One example of such innovation is the introduction of ephemeral regional orders in several equity markets (e.g., FLASH orders on NASDAQ, BOLT orders on BATS) that provide regional market participants the opportunity to view specific orders prior to public advertisement. Another example of such innovation is implied liquidity in several commodity markets (e.g. CME, ICE) that allow market participants to trade against synthetic orders whose price is derived from other derivative instruments. In order to capture and distinguish this type of order or price level in an order book, the inventors define the concept of attributes and apply this concept to the data structures employed by various embodiments disclosed herein. Each entry in an order book or price book may have one or more attributes. Conceptually, attributes are a vector of flags that may be associated with each order book or price book entry. By default, every order or aggregated price level is “explicit” and represents a limit order to buy or sell the associated financial instrument entered by a market participant. In some equity markets, an order or price level may be flagged using various embodiments disclosed herein with an attribute to indicate whether the order or price level relates to an ephemeral regional order (ERO). Similarly, in some commodity markets, an order or price level may be flagged using various embodiments disclosed herein to indicate whether the order or price level relates to an implied liquidity.
By capturing such attributes in the data structures employed by exemplary embodiments, the inventors note that these attributes thus provide another dimension to the types of book views that various embodiments disclosed herein generate. For example, one commodity trading application may wish to view a price aggregated book that omits implied liquidity, another commodity trading application may wish to view a price aggregated book with the explicit and implied price levels shown independently (spliced view), while another commodity trading application may wish to view a price aggregated book with explicit and implied entries aggregated by price. These three examples of attribute-based book views are shown in FIGS. 5, 6 and 7, respectively.
Thus, in accordance with an exemplary embodiment, the inventors disclose the use of attribute filtering and price level merging to capture the range of options in producing book views for books that contain entries with attributes. Attribute filtering allows applications to specify which entries should be included and/or excluded from the book view. Price level merging allows applications to specify whether or not entries that share the same price but differing attributes should be aggregated into a single price level.
The inventors also disclose several embodiments wherein a coprocessor can be used to enrich a stream of limit order events pertaining to financial instruments with order book data, both stream view order book data and summary view order book data, as disclosed herein.
These and other features and advantages of the present invention will be described hereinafter to those having ordinary skill in the art.